The State of AI in 2023: The Breakout Year of Creative AI

In 2023, artificial intelligence has seen peak advancements, creating a new landscape and marking the year as a breakthrough for generative AI.

The State of AI in 2023: The Breakthrough Year for Generative AI

In 2023, artificial intelligence has seen peak advancements, creating a new landscape and marking the year as a breakthrough for generative AI.

Highlights from the McKinsey Survey:

• The Boom of Generative AI: One-third of companies are using generative AI, with active involvement from C-suite leaders and an expected increase in overall investment in AI.
• Generative AI as a Business Focus: Generative AI has become a topic of interest for business leaders, rather than just a technical field.
• Early Days of Risk Management: Although generative AI has the potential to produce errors, very few companies proactively mitigate this risk.
• High-Performance AI Leads: Companies that have succeeded with traditional AI will be at the forefront of adopting generative AI.
• Impact on the Workforce: Generative AI could result in significant changes to the workforce, including job reductions in some areas and a need for reskilling.
• Opportunities and Challenges: Generative AI presents both opportunities and challenges for businesses.
• Prioritizing Risk Management: Businesses need to prioritize risk management while adapting to workforce changes and maximizing the potential of AI.

1. Generative AI Usage is Still in the Early Stages but Has Become Popular.

   • Rapid Adoption: Despite being newly launched, one-third of companies are using generative AI regularly, and 40% plan to increase overall investment in AI due to its potential.
   • Widespread Interest: 79% have had some exposure to generative AI, with 22% using it regularly, showing interest across sectors, industries, and experience levels.
   • Business Impact: The most common applications of generative AI are in marketing, sales, product development, and customer service, indicating high potential value.
   • Anticipated Disruptive Change: 75% predict significant disruption in the industry within three years, especially in technology and financial services.
   • Knowledge-Based Industries Ready for Significant Impact: Technology, banking, pharmaceuticals, and education are expected to see substantial increases.
   • Unresolved Risks: Very few companies have policies or strategies to mitigate inaccuracies, the most frequently mentioned risk associated with generative AI.
   • Conclusion: Generative AI is thriving, promising major transformations across industries. While excitement is high, companies must actively address potential risks such as inaccuracies to fully harness the technology's potential.

2. Leading Companies are at the Forefront of Generative AI Adoption.

   • High-Performance AI: Organizations investing heavily in AI are leading in both traditional AI and advanced generative AI.
   • Focus on Generative AI: High-performing companies are using generative AI more extensively, particularly in product development, risk management, and supply chain optimization.
   • Beyond Cost Reduction: Their generative AI goals prioritize creating new business models and enhancing existing product value, rather than merely focusing on cost reduction.
   • Wider AI Adoption: High-performing companies invest more in AI, integrating it into more functions and utilizing advanced capabilities like knowledge graphs.
   • Diverse Challenges: Despite facing unique difficulties in managing models and MLOps, high-performing companies have overcome fundamental barriers to AI adoption.
   • Need for Improvement: Even among this leading group, best practices such as real-time model monitoring and prompt issue resolution are still not fully developed.
   • Conclusion: Essentially, high-performance AI companies are actively innovating with generative AI, prioritizing its potential for new business models and product value creation, rather than solely focusing on cost reduction. However, even these leaders struggle with optimizing model management and applying advanced MLOps practices for safe and effective generative AI deployment.

3. The Impact of AI on the Workforce is Expected to be Significant, Requiring Talent Changes Related to AI.

   • A recent survey shows changes in how organizations allocate personnel for their AI initiatives. While traditional AI roles such as data engineers, machine learning engineers, and AI data scientists remain in high demand, the landscape is evolving in two main trends:
     • Decline in AI Software Engineer Roles: Recruitment rates for this position have dropped from 39% (last year) to 28% (this year), indicating a shift in focus from building AI infrastructure to utilizing and refining advanced AI, such as generative models.
     • Rapid Increase in Demand for Prompt Engineers: The need for prompt engineers is rising as advanced AI becomes more prevalent. Currently, 7% of respondents report hiring for this role.
   • Conclusion: Organizations continue to prioritize data and machine learning expertise but are also adapting to the demand for advanced technologies like prompt engineering. The shift from AI software engineers to prompt engineers reflects a change in focus from building AI infrastructure to utilizing and refining advanced AI.

4. The Adoption and Impact of AI Remain Steady, Despite All Attention on Generative AI.

   • Generative AI Adoption Slows Overall Progress: Despite the buzz, generative AI tools have yet to significantly drive overall AI adoption. The rate of organizations using AI remains stable at 55%, indicating that AI adoption has not achieved the expected growth rate.
   • Limited Scope of AI Usage: Only a minority (under 30%) use AI across multiple business functions, suggesting that AI deployment remains narrow. Product/service development and services continue to be the top use cases, similar to previous surveys.
   • Familiar Domains Lead: Product/service development and services remain the primary use cases, indicating that organizations are focusing on using AI in areas where they already have experience and knowledge.
   • Value Capture is Still in Early Stages: Only 23% believe that EBIT over 5% is attributable to AI, indicating significant untapped potential. This suggests that organizations are still in the early stages of capturing value from AI.
   • Ongoing ROI and Future Investment: Organizations see value in current AI applications and plan to increase investment in the coming years. This indicates that organizations trust in AI's potential and are committed to investing in this technology.
   • Revenue Growth Across Functions: Most businesses using AI report increased revenue related to AI. This indicates that AI is delivering positive results for organizations.
   • Increased AI Spending on the Horizon: More than two-thirds expect their organizations to boost AI investment within three years. This suggests that AI will continue to be an important investment area in the future.
   • Conclusion: While generative AI attracts much attention, broader AI adoption continues to follow a measured path. Organizations are realizing positive returns from current usage and plan to invest more, but the full potential for value creation from AI has yet to be fully realized.

5. About the Research

   • The online survey was conducted from April 11 to April 21, 2023, and received responses from 1,684 participants representing a full range of regions, industries, company sizes, functional expertise, and tenure. Among the respondents, 913 indicated that their organization had adopted AI in at least one function and were asked about their organization's use of AI. To adjust for response rate differences, the data was weighted according to each responding country's contribution to global GDP.